Longitudinal network data are increasingly available, allowing researchers to model how networks evolve over time and to make inference on their dependence structure. In this paper, a dynamic latent space approach is used to model directed networks of monthly interbank exposures. In this model,...

Standard spatial time-series models for financial networks can fail substantially in uncovering empirical network and risk dynamics. We propose a novel empirical spatial modeling framework that solves this problem by accommodating both heterogeneity and time-variation in economic connections and...